1. Field of Art
The present invention generally relates to the field of digital imaging, and more specifically, to methods of text recognition for use with images having comparatively little text.
2. Background of the Invention
Recognizing text within a digital image is a useful capability made possible by modern computer systems. Conventional optical character recognition (OCR) algorithms are designed for tasks such as recognizing text within an image—hereinafter referred to as a textually “dense” image—that includes large blocks of regularly-spaced text. For example, textually dense images include digital scans or photographs of pages of a book or magazine, where the text is arranged into columns, paragraphs, lines, and other regular and predictable units of text and occupies the majority, or at least a very sizeable portion, of the image.
However, there are a number of situations in which an image has little text compared to the overall size of the image—i.e., the image is textually “sparse”—and the text is not arranged in predictable units, yet recognition of the small amount of text is still beneficial. For example, a person taking a digital photo of a restaurant on her mobile phone might wish to look up information about the restaurant using the name painted on the restaurant building. As another example, a person taking a digital photo of a street scene using his mobile phone might wish to be presented with the option of dialing a phone number appearing in a billboard within the photo.
Conventional OCR algorithms, which are designed for recognition of text in textually dense images, have several shortcomings when applied to textually sparse images. First, conventional OCR algorithms have relatively poor performance when analyzing textually sparse images, since they perform the same text analysis across the entire image, even though only a small portion of it contains text. Second, conventional OCR algorithms have less than desirable accuracy for textually sparse images. For example, textures or other graphical patterns adjacent to a portion of text may cause the conventional OCR algorithm to fail to recognize that portion as text, instead incorrectly interpreting it to be part of the texture. Conversely, an OCR algorithm will also sometimes incorrectly interpret a non-textual graphical pattern to constitute a small portion of text, e.g. one or two characters. Thus, conventional OCR algorithms tend both to fail to recognize genuine text, and to incorrectly “recognize” small amounts of spurious text.